求解大规模电网在线稳定评估的广泛内核CVM算法

Extensive kernel core vector machine method for on-line stability assessment of large-scale power system

针对SVM等各类传统算法耗时过长,无法满足在线要求的问题,提出了一种基于广泛内核核向量机(ECVM)的大规模电力系统在线稳定评估算法。首先基于决策树算法对原始特征量进行特征筛选,然后基于ECVM分类器快速给出电力系统稳定状态的评估结果。该算法简化了最小闭包球问题中新球心的计算过程,避免了每次迭代都要解决QP问题,降低了算法的复杂度。在New England 39节点系统和某实际系统下的仿真结果表明了所提算法的优越性,为大规模电力系统的在线稳定评估提供了新思路。

As traditional algorithm such as SVM is time-consuming and unable to meet the requirements of assessment online, this paper proposes an extensive kernel core vector machine （ECVM） based on-line stability assessment algorithm of large-scale power system. Firstly, the original feature is extracted based on the decision tree algorithm and then a quick assessment conclusion is given based on ECVM classifier. This algorithm simplifies the new globe calculation process in the minimum enclosing ball issues to avoid the QP problem resolved at each iteration so that the complexity of algorithm is reduced. The simulation results in the New England 39-bus system and a real power system show the superiority of the proposed algorithm, which provides a new idea for the online stability assessment of large-scale power system.